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Type: Theses
Title: Constructive spiking neural networks for simulations of neuroplasticity
Author: Lightheart, Toby Asher
Issue Date: 2018
School/Discipline: School of Mechanical Engineering
Abstract: Artificial neural networks are important tools in machine learning and neuroscience; however, a difficult step in their implementation is the selection of the neural network size and structure. This thesis develops fundamental theory on algorithms for constructing neurons in spiking neural networks and simulations of neuroplasticity. This theory is applied in the development of a constructive algorithm based on spike-timing- dependent plasticity (STDP) that achieves continual one-shot learning of hidden spike patterns through neuron construction. The theoretical developments in this thesis begin with the proposal of a set of definitions of the fundamental components of constructive neural networks. Disagreement in terminology across the literature and a lack of clear definitions and requirements for constructive neural networks is a factor in the poor visibility and fragmentation of research. The proposed definitions are used as the basis for a generalised methodology for decomposing constructive neural networks into components to perform comparisons, design and analysis. Spiking neuron models are uncommon in constructive neural network literature; however, spiking neurons are common in simulated studies in neuroscience. Spike- timing-dependent construction is proposed as a distinct class of constructive algorithm for spiking neural networks. Past algorithms that perform spike-timing-dependent construction are decomposed into defined components for a detailed critical comparison and found to have limited applicability in simulations of biological neural networks. This thesis develops concepts and principles for designing constructive algorithms that are compatible with simulations of biological neural networks. Simulations often have orders of magnitude fewer neurons than related biological neural systems; there- fore, the neurons in a simulation may be assumed to be a selection or subset of a larger neural system with many neurons not simulated. Neuron construction and pruning may therefore be reinterpreted as the transfer of neurons between sets of simulated neurons and hypothetical neurons in the neural system. Constructive algorithms with a functional equivalence to transferring neurons between sets allow simulated neural networks to maintain biological plausibility while changing size. The components of a novel constructive algorithm are incrementally developed from the principles for biological plausibility. First, processes for calculating new synapse weights from observed simulation activity and estimates of past STDP are developed and analysed. Second, a method for predicting postsynaptic spike times for synapse weight calculations through the simulation of a proxy for hypothetical neurons is developed. Finally, spike-dependent conditions for neuron construction and pruning are developed and the processes are combined in a constructive algorithm for simulations of STDP. Repeating hidden spike patterns can be detected by neurons tuned through STDP; this result is reproduced in STDP simulations with neuron construction. Tuned neurons become unresponsive to other activity, preventing detuning but also preventing neurons from learning new spike patterns. Continual learning is demonstrated through neuron construction with immediate detection of new spike patterns from one-shot predictions of STDP convergence. Future research may investigate applications of the developed constructive algorithm in neuroscience and machine learning. The developed theory on constructive neural networks and concepts of selective simulation of neurons also provide new directions for future research.
Advisor: Grainger, Steven Drummond
Lu, Tien-Fu
Dissertation Note: Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 2018
Keywords: constructive neural networks
spiking neurons
neural simulation
pattern detection
one-shot learning
continual learning
Provenance: This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at:
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